Solving problems in bioinformatics often needs extensive computational power. Current trends in processor architecture, especially massive multi-core processors for graphic cards, combine a large number of cores into a single chip to improve the overall performance. The Compute Unified Device Architecture (CUDA) provides programming interfaces to make full use of the computing power of graphics processing units. We present a way to use CUDA for substantial performance improvement of methods based on multi-dimensional scaling (MDS). The suitability of the CUDA architecture as a high-performance computing platform is studied by adapting a MDS algorithm on specific hardware properties. We show how typical bioinformatics problems related to dimension reduction and network layout benefit from the multi-core implementation of the MDS algorithm. CUDA-based methods are introduced and compared to standard solutions, demonstrating 50-fold acceleration and above.